In this study, we use MovieLens Dataset [1]}, which is one of the well-known movie recommendation services. In this dataset, movies with related information such as movie titles, release date, and genre are provided. Each content is requested by several users in different timestamps, where the contextual information of users such as age, gender, occupation, and their ZIP codes are also released. With the assumption that users leave a comment after watching a movie [2]}, [3]}, [4]} and in order to extract the content request pattern, commenting on a content is considered as a request. Moreover, to identify the users' location in each timestamp, ZIP codes are converted to longitude and latitude coordinates [2]}. Considering the limited transmission range of hgNBs, hgNBs' locations, and users locations, the available hgNBs for serving requests of all users will be determined. Our main goal in the TEDGE caching framework is to monitor the historical requests pattern of each content to predict the Top-\(K\)  popular content in an upcoming time period. Therefore, the preparation of the dataset is performed in the following four steps:
